GraphQL vs REST
REST and GraphQL approach API design from fundamentally different angles. REST treats the server as the authority on response shape. Each endpoint (/users, /orders/123) returns a defined set of fields. The server decides what data is available and how it is structured. Clients adapt to the server's shape. Adding a new field is backward-compatible; removing one is a breaking change. GraphQL inverts this: the client declares exactly which fields it needs in the query. The server executes the query against a typed schema and returns only the requested fields. This eliminates over-fetching (receiving unused data) and under-fetching (needing multiple requests to assemble a view). The N+1 problem is GraphQL's most common production issue: a naive resolver fetches one DB query per list item. The DataLoader pattern (batch + deduplicate) solves this but requires explicit implementation. Introspection is GraphQL's superpower for tooling: clients can query the schema itself, enabling auto-generated docs, type-safe client generation, and IDE completion. REST achieves similar tooling with OpenAPI specs, but OpenAPI must be maintained separately while GraphQL introspection is always in sync with the implementation.
REST organizes APIs around resources and HTTP methods, with fixed response shapes per endpoint. GraphQL uses a single endpoint where clients declare exactly which fields they need. REST is the dominant API style (92% enterprise adoption) and the right default for most public APIs. GraphQL solves over-fetching and under-fetching for complex, client-driven data requirements – primarily in frontend-heavy applications, mobile apps, and API gateways aggregating multiple services.
| Feature | GraphQL | REST |
|---|---|---|
| Endpoints | One endpoint for all operations | One endpoint per resource (/users, /orders) |
| Request format | POST with query in body (or GET for simple) | HTTP methods (GET/POST/PUT/PATCH/DELETE) |
| Response shape | Client-defined – request only needed fields | Server-defined – fixed shape per endpoint |
| Over-fetching | Eliminated – client requests exact fields | Common – endpoint returns all fields regardless |
| Under-fetching | Eliminated – one request for related data | Common – multiple requests to assemble a view |
| Versioning | Usually not needed – schema evolution via deprecation | URL versioning (/v1/, /v2/) or header versioning |
| Type system | Strongly typed schema (SDL) – validated at runtime | No built-in types – OpenAPI spec optional |
| Caching | HTTP caching disabled by default (POST body) | GET responses cacheable by browser, CDN, proxy |
| Error handling | HTTP 200 always; errors in errors[] array | HTTP status codes (400, 404, 500) for errors |
| File uploads | Not in spec – use multipart workarounds | Native multipart/form-data support |
| Real-time | Subscriptions (WebSocket or SSE) | SSE, WebSocket, or polling |
| Learning curve | Higher – schema, resolvers, N+1, DataLoader | Lower – HTTP methods, status codes, URLs |
| Tooling | GraphiQL, Apollo Studio, codegen from schema | Swagger/OpenAPI UI, Postman, curl |
When to use GraphQL
GraphQL is the right choice for: complex, client-driven data requirements where different clients (mobile, web, TV) need different data shapes, API gateways that aggregate data from multiple microservices into a unified schema, rapid product iteration where frontend teams need to evolve data requirements without backend changes, and any scenario with significant over-fetching or under-fetching problems.
When to use REST
REST is the right choice for: public APIs consumed by third parties (GraphQL's schema complexity and non-standard error handling create adoption friction), simple CRUD APIs, file upload endpoints, any scenario where HTTP caching is important (REST GET responses are naturally cacheable), and teams new to API development (REST has a lower learning curve and more universal tooling).
Common Mistakes
- Using GraphQL for simple CRUD APIs – the N+1 problem, DataLoader setup, and schema maintenance add complexity that REST handles trivially. If your API is straightforward CRUD over a database, REST is simpler.
- Exposing database schema directly in GraphQL – GraphQL's type flexibility tempts teams to mirror the DB schema in the API. This creates tight coupling and exposes internal details. Design the GraphQL schema around client needs, not database structure.
- Not handling the N+1 problem – a list query that fires one DB query per item is a common production performance issue. Always implement DataLoader for list resolvers before shipping a GraphQL API to production.
- Disabling GraphQL introspection in production without replacing it with documentation – introspection is used by client tooling. If you disable it for security, provide an alternative discovery mechanism.
FAQ
Can GraphQL and REST coexist in the same API?
Yes, and this is common. Many teams expose a REST API for simple public access and a GraphQL API for internal client teams. Some use a GraphQL gateway that federates multiple REST microservices into a unified schema (Apollo Federation, Hasura).
Is GraphQL faster than REST?
Not inherently. GraphQL can reduce the number of round trips (one request instead of three) and payload size (only requested fields). But naive GraphQL resolvers with N+1 queries are slower than a single optimized REST endpoint. Performance depends on resolver implementation, not the protocol choice.